The Rise of Adaptive Platform Trials in Critical Care

As durable learning research systems, adaptive platform trials represent a transformative new approach to accelerating clinical evaluation and discovery in critical care. This Perspective provides a brief introduction to the concept of adaptive platform trials, describes several established and emerging platforms in critical care, and surveys some opportunities and challenges for their implementation and impact.

effect and partly because it is challenging to build a network of sufficient size to generate sample sizes that are adequate for the detection of small treatment effects in a timely manner.
To overcome these barriers, we need a new approach to conducting clinical research and clinical trials, one that adopts a longterm vision for a durable integrated collaborative research system.Such a system would build on the initial work required to set up a single trial-investigator network, governance and regulatory structures, data management systems-to create a long-term foundation for collaboration, discovery, and improvements in care.Methodological expertise and innovation can be layered on top of this foundation and applied across multiple trials to fundamentally enhance operations and insights.Operating a research system over the long term affords the opportunity to learn what does and does not work and to progressively improve efficiency and effectiveness.Future trials within the system could leverage these methods and learning for greater success, efficiency, and insights.Indeed, information about patient characteristics and outcomes can be literally "borrowed" to enhance the precision of treatment effect estimates in later trials.This research system and culture of learning through experience manifests in adaptive platform trial designs that use information accumulating in the trial to ensure that the trial operating characteristics (e.g., sample size) afford clear conclusions to inform clinical decisions.Adaptive platform trials aim to be durable learning research systems (3)(4)(5).Larger upfront investment in terms of time and money may be required for an adaptive platform trial, but, over the long term, provide a foundation for more efficient collaboration, discovery, and improvements in care (6).
Adaptive platform trials have been around for well over a decade (7), but gained wide attention during the coronavirus disease 2019 (COVID-19) pandemic, with demonstrated success in the rapid evaluation of multiple therapies (see REMAP-CAP below).The pandemic also provided demonstrative potential for multiplatform trials, whereby the same intervention protocol was applied across multiple platforms and analyzed as a single trial to maximize global coverage and accelerate trial conclusions (8).Building on this experience, several adaptive platform trials are now being initiated to investigate new treatments and strategies for critical illness.Here we briefly summarize them, highlighting key areas of focus and unique methodological features (Table 1).

REMAP-CAP
The REMAP-CAP (Randomized Embedded Multifactorial Adaptive Platform trial for Community Acquired Pneumonia) is an established pioneer in adaptive platform trials (www.remapcap.org).Originally conceived after the influenza H1N1 pandemic in 2009, REMAP-CAP has recruited more than 13,000 patients with severe community-acquired pneumonia since 2016 from more than 300 hospitals worldwide, on every continent.It is an ongoing, investigator-initiated platform trial

PANTHER
The overall aim of PANTHER (Precision Medicine Adaptive Platform Network Trial in Hypoxemic Acute Respiratory Failure) is to accelerate the development of pharmacological therapies for acute hypoxemic respiratory failure and acute respiratory distress syndrome (ARDS) by establishing an international phase II precision medicine adaptive platform trial (https://panthertrial.org).PANTHER will examine patients with hyper-and hypoinflammatory phenotypes of ARDS (13) to answer the question whether, compared with usual care alone, simvastatin or an additional biologically targeted intervention (to be determined) increases organ failure-free days at Day 28 in patients with ARDS as defined by the newly published global definition (14).PANTHER will use a Bayesian adaptive multiarm trial design with predefined triggers for efficacy and futility.Regular adaptive analyses will enable investigators to identify differential treatment responses across phenotypes by examining treatment effect in phenotype strata and stopping arms in the case of evidence of futility or efficacy.Inflammatory phenotype will be prospectively determined in real time before randomization using a validated algorithm based on plasma IL-6, soluble tumor necrosis factor receptor-1, and bicarbonate (15).PANTHER will also collect extensive biological samples, allowing further mechanistic studies to be conducted in parallel with the conduct of the platform trial, potentially allowing further phenotypes and interventions to be added to the platform over time.The key design features include a dataenabled, embedded, multiarm, umbrella, adaptive platform trial within a Bayesian analytic framework (ISRCTN Registry no.82395639).The primary objective is to evaluate the efficacy of experimental treatments on prespecified outcomes in critically ill adult patients who meet the eligibility criteria for a specific syndromeagnostic treatable trait (defined in Table 1) compared with usual care.All analyses will be performed using Bayesian ordinal regression models to calculate treatment effects of active interventions (vs.control), and these analyses will adjust for prespecified prognostic variables, with priors informed by accruing trial data as well as external information.For sequential learning with interim adaptive analyses, the trial has prespecified intermediate outcomes for each trait (stage 1 analyses).When interventions reach the prespecified maximum sample size or meet the criteria for efficacy or futility, based on the posterior probability of clinically important efficacy, in stage 1 analyses, the treatment effect will be reported for the trial primary outcome of organ support-free days to Day 21 (stage 2 analyses).The data-enabled design uses existing data-capture frameworks within the National Health Service in Scotland to streamline the data collection and create models for efficient data flow in clinical trials.The embedded biological sampling uses systems immunology approaches (18) to study the mechanisms of intervention effect and enable discoveries such as new treatable traits, biological heterogeneity of treatment effect, and biomarkers.

Challenges for Adaptive Platform Trials in Critical Care
Each of these research systems will grow and develop over time to execute clinical trials more cost-effectively and efficiently (6), ultimately achieving higher-quality, more informative trial results to guide clinical decisions for patients and families.Adaptive platform trials should not, however, be viewed as a panacea.Despite the strengths of platforms and the opportunities they present, Challenging to ensure appropriate academic recognition for all participating investigators given the substantial team effort involved in running these trials; academic institutions may need to evolve to place higher value on collaborative efforts they have relevant weaknesses and face important threats (Table 2).Some of the methods and analytical strategies remain unfamiliar to the broad clinical community, potentially hampering knowledge translation.The regulatory and funding mechanisms created for traditional clinical trials must evolve to ensure that adaptive platform trials can maximize the potential benefits they offer to investigators, the clinical community, and patients.
A commitment to collaboration is perhaps the crucial ingredient in the continued progress and long-term success of these research systems.These collaborations must develop on several fronts.First, "within-platform" collaboration is essential.Clinical research and clinical trials in general are team sports, but platform trials in particular require investigators to surrender some degree of autonomy to ensure that different trials and domains within the platform fit together seamlessly.Second, "between-platform" collaboration is advantageous.When appropriate opportunities arise, it may be more efficient for platforms to leverage the networks and expertise represented by other platforms to expand the reach of their trials, as exemplified by the concept of the multiplatform trial.Platforms should also maintain strong lines of communication to minimize competition or conflict between their operations.Working together to align data collection and outcomes will maximize the long-term value of information in platforms and facilitate multiplatform trial collaborations.To this end, REMAP-CAP, PANTHER, and PRACTICAL have worked closely to align and standardize their biological sampling procedures.Third, "beyond-platform" collaboration is crucial.These tight-knit research communities must proactively work to be outward-looking and welcoming to those interested in joining the platforms.Investigators early in their careers should find platform trials an especially beneficial development; by working within platforms, they are able to leverage the trial infrastructure already created to undertake their studies and trials and answer their research questions within a supportive and well-mentored research environment.Platforms will need to consider strategies to communicate the invitation to join their research system to the broader clinical and scientific community in critical care.
In sum, adaptive platform trials hold a great deal of potential for the field of critical care.To make the most of that potential, a great deal of time, effort, and funding must be invested, and a deep commitment to collaboration must be sustained.

Table 1 .
Definitions of Terms Employed in Different Adaptive Platform Trials critical illness syndromes.Stated differently, the same treatable trait can be found in different critical illness syndromes such as sepsis, pancreatitis, trauma, and acute respiratory distress syndrome.In the TRAITS trial, critical illnesses are grouped into treatable traits identified by combining a biomarker and a bedside clinical variable of organ dysfunction.Thus, trait identification is unaffected by issues associated with diagnostic test performance and probabilistic allocations.Critically ill patients can have more than one trait concurrently.

Table 2 .
Considerations for Adaptive Platform Trials in Critical Care Category Consideration Strengths Capacity to leverage shared, standardized governance, methods, and analytical infrastructure across multiple trials for long-term operational efficiency (easier to conduct additional new trials) and quality (infrastructure will be rigorously evaluated and improved over time) Use of a common or shared control group can significantly reduce net sample size requirement across multiple interventional trials When patients are randomized in multiple domains, can evaluate the effects of combinations of interventions (including interactions between interventions) Response-adaptive randomization can help ensure the probability of receiving treatment aligns with the most current degree of equipoise as to benefit or harm Potentially fewer legal and contractual interinstitutional agreements (though the individual agreements are Transparent governance is critical to ensure fairness, equity, accessibility, rigor, and openness Critical to communicate open posture to enable new investigators to join and make use of the platform Ensuring timely and genuinely informed consent for patients or substitute decision-makers presented with the opportunity to be randomized to multiple interventions across multiple domains